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Challenges in mesoscale modelling for offshore wind energy

Speaker: Xiaoli Guo Larsén (xgal@dtu.dk)

Resource and extremes, etc.

1

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From 4coffshore.com

ResourceSiting/design/extremesForecastingOperation/Maintenance…

Relevance

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From 4coffshore.com

ResourceSiting/design/extremesForecastingOperation/Maintenance…

ResourceSiting/design/extremesForecastingOperation/Maintenance…

- Missing farms and their impact

- Missing accuracy in physical processes cross scales, e.g. coastal flow, farm wake vs. turbine wakes…

State-of-the-art: resources/challenges

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Wind farm cluster effects

Satellite SAR shows wind farm wakes

Hasager, C. B., Vincent, P., Badger, J., Badger, M., Di Bella, A., Pena Diaz, A., ... Volker, P. (2015). Using Satellite SAR to Characterize the Wind Flow around Offshore Wind Farms. Energies, 8(6), 5413-5439. DOI:10.3390/en8065413

RADARSAT-2 from Data and Products © MacDonald, Dettewiler and Associates Ltd

WRF shows wind farm wakes

WRF-EWP minus WRF

Courtesy: Du, Volker and Larsén (2018): OffshoreWake project report WP3 (the simulation is done using COAWST modeling system, mostly by Du J)

State-of-the-art: resources

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Mesoscale modelling of Thornton Bank (BE) and Belwind (BE)

Courtesy: Patrick Volker

State-of-the-art: resources

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State-of-the-art: resources

Larsén X.G., Volker P., Imberger M., Fischereit J., Langor E., Hahmann A., Ahsbahs T., Duin M., Ott S., Sørensen P., Koivisto, M., Maule P., Hawkins S., Kishore A. and Du J. (2019): Calculation of wakes from offshore wind farm cluster and the introduction to the Danish power integration system. WindEurope Offshore 2019, Copenhagen.

DTU’s OffshoreWake Calcultion System: WRF+SWAN+WAKE+POWER BALANCING

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From 4coffshore.com

ResourceSiting/design/extremesForecastingOperation/Maintenance…

- Missing the ability in capturing the extremes

- Missing the ability in accurately calculating the interactions between wind, wave and ocean during extreme conditions

- Missing measurements for building up understanding of physical processes during storms

State-of-the-art: extremes/challenges

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State-of-the-art: extremes/challengesOur efforts target at:1. capturing the storms – wind and waves[1,2,3,4,5]

2. capturing the key statistics of extremes[5,6]

[1] Larsén X., Du J. Bolanos R., Badger M., Kelly M. and Larsen S. Estimation of offshore extreme wind from wind-wave coupled modeling, Wind Energy DOI:10.1002/we.2339, vol 22, Issue 8.

CD

Wind speed at 10 m (m/s)

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WRF

SWANwww.xwiwa.dk

JanssenFan

Taylor&YellandDrennan

OostLiu

WBLM[1-3]

2 Du J., Bolanos R. and Larsén X. (2017) The use of a wave boundary layer model in SWAN. Journal of Geophysical Research - Ocean: DOI: 10.1002/2016JC012104, vol. 122, No 1, p42 - 62.3. Du. J., Bolanos R., Larsén X. and Kelly M. 2019: Wave boundary layer in SWAN revisited. Ocean Science, ISSN 1812-0784, https://www.ocean-sci-discuss.net/os-2018-90/.4. Larsén X., Du J., Bolanos R. and Larsen S. 2017: On the impact of wind on the development of wave field during storm Britta. Ocean Dynamics 67:1407-1427, DOI 10.1007/s10236-017-1100-1.5. Imberger M., Larsén X., Davis N. and Du J. 2020: Approaches toward improving the modelling of mid-latitude cyclones entering at the lateral boundary corner in the limited area model WRF, Quarterly Journal of the Royal Meteorological Society. DOI:10.1002/qj.3843.

State-of-the-art: extremesOur efforts target at:1. capturing the storms – wind and waves[1,2,3,4,5]

2. capturing the key statistics of extremes[5,6]

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1. FINO12. FINO23. FINO34. Høvsøre5. Horns Rev

State-of-the-art: extremes

[1] Larsén X., Du J. Bolanos R., Badger M., Kelly M. and Larsen S. Estimation of offshore extreme wind from wind-wave coupled modeling, Wind Energy DOI:10.1002/we.2339, vol 22, Issue 8.

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6. Larsén X., Ott S., Badger J., Hahmann A. N. and Mann J. 2012: Recipes for correcting the impact of effective mesoscale resolution on the estimation of extreme winds. Journal of applied meteorology and climatology, Doi:10.1175/JAMC-D-11.090, vol 51, No. 3, p521-533.

State-of-the-art: extremesOur efforts target at:1. capturing the storms – wind and waves[1,2,3,4,5]

2. capturing the key statistics of extremes[5,6]

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State-of-the-art: extremes

50-year wind at 10 m, 50 m, 100 m, 150 m globally

From our GASP project

Our efforts target at:1. capturing the storms – wind and waves[1,2,3,4,5]

2. capturing the key statistics of extremes[5,6]

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50-year wind: Tropical Cycloneaffected areas

Method: Reanalysis data + Spectral Correction Method + Best Track machine learning7

[7] Larsén X. and Ott S. 2020: Extreme winds in tropical cyclone affected water areas, DTU Wind Energy E-Report-0206, draft.

State-of-the-art: extremesOur efforts target at:1. capturing the storms – wind and waves[1,2,3,4,5]

2. capturing the key statistics of extremes[6,7]

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Knowledge gap and future outlook

Accuracy

• Developing/collecting/using measurements with good target

• Understanding physical processes• Combining physical and statistical approaches

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Acknowledgement

Thanks to the contributions from colleagues (see names in publications) and support from projects: GASP, OffshoreWake (www.offshorewake.dk), X-WIWA (www.xwiwa.dk), WASA etc.

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Appendix

16

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State-of-the-art: extremes/challenges

https://mattloftus.github.io/2016/02/03/threejs-p2/

www.wasp.dk

Our efforts target at:1. capturing the storms – wind and waves[1,2,3,4,5]

2. capturing the key statistics of extremes[5,6]

[1] Larsén X., Du J. Bolanos R., Badger M., Kelly M. and Larsen S. Estimation of offshore extreme wind from wind-wave coupled modeling, Wind Energy DOI:10.1002/we.2339, vol 22, Issue 8.